AI/LLM Cheatsheet 19 — Building Chat UI
Cheatsheet: chat UI, streaming, markdown rendering, code blocks.
AI
AI/LLM Cheatsheet 20 — Production LLM App
Cheatsheet: full prod LLM app stack.
Self-Hosting LLMs in 2026 — When the Math Actually Works
Practical LLM self-hosting math: GPU pricing, throughput per GPU, sustained load break-even, vLLM tuning, and when API still wins.
Anthropic API Best Practices in 2026 — Caching, Tool Use, Streaming, and Production Patterns
Practical Anthropic API: prompt caching tactics, tool use loops, streaming, batch API, retries, and pitfalls from real production deployments.
Evaluating AI Coding Tools in 2026 — Benchmarks That Matter and Ones That Don't
Practical AI coding eval: SWE-bench / live benchmarks, internal benchmarks on your codebase, productivity metrics, and what to ignore.
Synthetic Data with LLMs in 2026 — Use Cases, Risks, and the Patterns That Work
Practical synthetic data: fine-tune training data, eval set generation, edge case enumeration, and the model-collapse / quality risks to watch.
Voice Agents in 2026 — STT, LLM, TTS, and Latency That Doesn't Hurt
Practical voice agent architecture: streaming Deepgram/AssemblyAI → LLM → ElevenLabs/OpenAI TTS, latency budgeting, barge-in, and patterns from production calls.
Model Context Protocol (MCP) in 2026 — What It Solved, What It Didn't
Practical MCP: building an MCP server, integrating with Claude / Cursor, when MCP wins, and the security pitfalls of remote tool access.
LLM Tool Use Patterns in 2026 — Schemas, Validation, and the Loop
Practical LLM tool use: schema design, parallel tool calls, error/retry on bad inputs, tool result formatting, and patterns that scale beyond 5 tools.
Agentic Coding in 2026 — Claude Code, Cursor, and the Real Workflow
Honest take on AI coding agents: where Claude Code / Cursor shine, when they hurt, the discipline of using them well, and what stays human.